Data analytics with managerial application ass 2Nishant Kumar
This presentation depicts insights of the article "Data Scientist: The Sexiest Job of the 21st Century", and also how these insight are relevant to a manager in india.
Data analytics with managerial application ass 2Nishant Kumar
This presentation depicts insights of the article "Data Scientist: The Sexiest Job of the 21st Century", and also how these insight are relevant to a manager in india.
A brief overview of the Rady School of Management at UC San Diego and how are developing ethical leaders for innovation-driven organizations. Find out if the Rady School is the MBA or Master of Finance program for you.
The presentation talks about "Data Science being the sexiest job of the 21st century". What are the challenges faced by the industry and how to Overcome them, is the main theme of the presentation
Explains: What is Data Science? What is the difference between Data Science and Data Engineering, and between Data Science and Business Intelligence? What type of work do Data Scientists do, and what types of companies employ them? What is the job outlook for Data Science? What professional education is required?
What do you need to succeed in working with Big Data? RedMonk analyst Donnie Berkholz will present quantitative research on the state of the field, covering the breadth of languages, tools, and infrastructure, to show you which choices to make today and which ones you'll need to get ready for, soon.
How to be data driven? How is LinkedIn using data in it's products. How can any company use data to become successful?
Why we are not missing data scientists but managers
Idiots guide to setting up a data science teamAshish Bansal
Some nuggets of how I started the data science practice at Gale Partners on a budget. Presented at the Toronto Hadoop Users Group (THUG) in April, 2015.
Ayasdi (ai-yaz-dee), a Silicon Valley start-up, has created technology that may prove to redefine an entire industry. Ayasdi provides a highly differentiated platform for data analysis based on the concept of Topological Data Analysis, first documented in the 1700’s – a platform that has the potential to shift the direction of future technology development. This case study briefly explores the “Big Data” industry as it is today, and the future implications that Ayasdi may have on the industry; including the strategic challenges Ayasdi has in positioning themselves as a contender and prospective leader within the “Big Data” and Enterprise Technology market segments.
Overview of AI and its patterns that could apply to business. Four patterns were discussed in the slides (adapted by Kavin Dewalt). Several AI/ Data Science Use Cases has been discussed.
Data Science Thailand
Data Cube
Stratosphere Intro (Java and Scala Interface)Robert Metzger
A quick walk overview of Stratosphere, including our Scala programming interface.
See also bigdataclass.org for two self-paced Stratosphere Big Data exercises.
More information about Stratosphere: stratosphere.eu
A brief overview of the Rady School of Management at UC San Diego and how are developing ethical leaders for innovation-driven organizations. Find out if the Rady School is the MBA or Master of Finance program for you.
The presentation talks about "Data Science being the sexiest job of the 21st century". What are the challenges faced by the industry and how to Overcome them, is the main theme of the presentation
Explains: What is Data Science? What is the difference between Data Science and Data Engineering, and between Data Science and Business Intelligence? What type of work do Data Scientists do, and what types of companies employ them? What is the job outlook for Data Science? What professional education is required?
What do you need to succeed in working with Big Data? RedMonk analyst Donnie Berkholz will present quantitative research on the state of the field, covering the breadth of languages, tools, and infrastructure, to show you which choices to make today and which ones you'll need to get ready for, soon.
How to be data driven? How is LinkedIn using data in it's products. How can any company use data to become successful?
Why we are not missing data scientists but managers
Idiots guide to setting up a data science teamAshish Bansal
Some nuggets of how I started the data science practice at Gale Partners on a budget. Presented at the Toronto Hadoop Users Group (THUG) in April, 2015.
Ayasdi (ai-yaz-dee), a Silicon Valley start-up, has created technology that may prove to redefine an entire industry. Ayasdi provides a highly differentiated platform for data analysis based on the concept of Topological Data Analysis, first documented in the 1700’s – a platform that has the potential to shift the direction of future technology development. This case study briefly explores the “Big Data” industry as it is today, and the future implications that Ayasdi may have on the industry; including the strategic challenges Ayasdi has in positioning themselves as a contender and prospective leader within the “Big Data” and Enterprise Technology market segments.
Overview of AI and its patterns that could apply to business. Four patterns were discussed in the slides (adapted by Kavin Dewalt). Several AI/ Data Science Use Cases has been discussed.
Data Science Thailand
Data Cube
Stratosphere Intro (Java and Scala Interface)Robert Metzger
A quick walk overview of Stratosphere, including our Scala programming interface.
See also bigdataclass.org for two self-paced Stratosphere Big Data exercises.
More information about Stratosphere: stratosphere.eu
Clare Corthell: Learning Data Science Onlinesfdatascience
Clare Corthell, Data Scientist and Designer at Mattermark, and author of the Open Source Data Science Masters, shares her experience teaching herself data science with online resources. http://datasciencemasters.org/
data scientist the sexiest job of the 21st centuryFrank Kienle
Invited talk, describing the exciting work at Blue Yonder (www.blue-yonder.com),
'congress smart services - new business models' in Aachen, Germany 2015
Close The Compassion Gap to Boost Resilience in Kidsanxietyreliefkids
We’d both been anxiously awaiting responses to the college applications we’d sent out earlier that year. Before the age of e-mail, this meant running to the mailbox every day or even several times a day to see if any letters had arrived.
Minne analytics presentation 2018 12 03 final compressedBonnie Holub
Monday was another great conference by MinneAnalytics! #MinneFRAMA was a great success with over 1,100 attendees at Science Museum of Minnesota. Alison Rempel Brown is a great host! A Teradata colleague told me that her post about my presentation "blew up" with hits and she got over 2K views, and 60+ likes. I'm proud to be a part of this great #datascience organization brining #machinelearning and #artificialintelligence #analytics to our #bigdata clients. If you want my slides, here they are.
Data-Ed Webinar: Demystifying Big Data DATAVERSITY
We are in the middle of a data flood and we need to figure out how to tame it without drowning. Most of what has been written about Big Data is focused on selling hardware and services. But what about a Big Data Strategy that guides hardware and software decisions? While virtually every major organization is faced with the challenge of figuring out the approach for and the requirements of this new development, jumping into the fray hastily and unprepared will only reproduce the same dismal IT project results as previously experienced. Join Dr. Peter Aiken as he will debunk a number of misconceptions about Big Data as your un-typical IT project. He will provide guidance on how to establish realistic Big Data management plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers without getting lost in the hype.
Takeaways:
- The means by which Big Data techniques can complement existing data management practices
- The prototyping nature of practicing Big Data techniques
- The distinct ways in which utilizing Big Data can generate business value
- Bigger Data isn’t always Better Data
We are in the middle of a data flood and we need to figure out how to tame it without drowning. Most of what has been written about Big Data is focused on selling hardware and services. But what about a Big Data Strategy that guides hardware and software decisions? While virtually every major organization is faced with the challenge of figuring out the approach for and the requirements of this new development, jumping into the fray hastily and unprepared will only reproduce the same dismal IT project results as previously experienced. Join Dr. Peter Aiken as he will debunk a number of misconceptions about Big Data as your un-typical IT project. He will provide guidance on how to establish realistic Big Data management plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers without getting lost in the hype.
Check out more of our Data-Ed webinars here: www.datablueprint.com/webinar-schedule
Online text data for machine learning, data science, and research - Who can p...Fredrik Olsson
This slide deck concerns online text data for machine learning, artificial intelligence, data science, and scientific research. After this talk, you’ll know who can provide online text data, what types of data are hard to get, and principal data hygiene factors.
Updated in August 2019.
Why is Data Science a Popular Career Choice.pdfUSDSI
Do you want to become the backbone of big corporates and giant business groups around the world? Beginning your career trajectory by grabbing the perfect spot in the data science certification courses provided around the world. The US Bureau of Labor Statistics projects 35.8% employment growth for data scientists till 2031, over a decade period beginning 2021. The growing use of machine learning and artificial intelligence technologies is another factor driving the demand for professionals skilled in data science.
Brimming with humungous career opportunities, the data science industry is set in motion to yield multitudinous growth opportunities across diversified industries worldwide. By automating procedures, increasing effectiveness, and allowing predictive capabilities, Artificial intelligence and machine learning algorithms hold the ability to change the entire landscape.
Data Science has become a fascinating career choice that calls for working closely with cutting-edge technology and addressing challenges. If you are someone who wishes to work with humungous data, has a passion for numbers, and has a clear vision of setting their career on a thriving path; data science is the right pick for you!
A diversified array of organizations is actively looking for data-hungry professionals who are coarsely skilled at data science to analyze data and churn out business decisions for the greater good of the company. Today is the ripe time to get started with a data science career, that promises an elevated trajectory and nothing else.
With the rise of technological innovations and industrial evolution, massive datasets become unmanageable. The future of such a massive explosion of data calls for an urgent appointment of qualified data scientists; enabling bigger business moves. This is where getting certified in the field makes sense.
Without wasting any further time, it is an advisable move to get certified in key data science skills that are sure to rage in the industry worldwide. Begin with the most trusted names in the data science certifications providers industry today!
https://www.usdsi.org/data-science-insights/why-is-data-science-a-popular-career-choice
Big Data; Big Potential: How to find the talent who can harness its powerLucas Group
Big Data is in its infancy but it holds great promise. The key to success is finding and keeping the talent with the skills necessary to obtain and analyze the data, ask the right questions, and present findings in a compelling fashion that makes sense for your organization.
This white paper: Analyzes the big data revolution and the potential it offers organizations. Explores the critical talent needs and emerging talent gaps related to big data. Offers examples of organizations that are meeting this challenge head on. Recommends four steps HR and talent management professionals can take to bridge the talent gap.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
2. The rise of Big Data
http://www.csc.com/insights/flxwd/78931-big_data_universe_beginning_to_explode
3. The rise of Big Data
Long list of companies
now corral and analyze
Big Data to gain a
competitive advantage
Limitless applications:
Recommender systems
(people you know,
suggested reading),
Sentiment analysis,
pricing analysis, etc.
4. The rise of Big Data
Much of the rise is
due to the new tools
we have to play with
5. Case Study: LinkedIn
• Jonathan Goldman,
Ph.D, LinkedIn, 2006
• 8 million members
and growing, but
members not seeking
new connections as
expected
• Goldman given
autonomy to test data
product prototype
• Conversions up 30%
7. Data Scientist Skills
Hard Skills
• Knowledge in Stats, Data
Mining, and Machine
Learning
• Open source tools such as R
and Python
• Data Visualization
• Data warehousing and
architecture
• Coding skills
Soft Skills
• Curiosity
• Storytelling
• Domain knowledge
• Problem Solving
8. How do businesses find applicants?
• Universities
• User Group Membership Lists
• Linkedin
• Technology Conference
• Venture Capitalist
• Host a Competition